"""
Module with functions for creating plots of
color-magnitude color-color diagrams.
"""
import json
import os
import pathlib
import warnings
from typing import Dict, List, Optional, Tuple, Union
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colorbar import Colorbar
from matplotlib.ticker import MultipleLocator
from scipy.interpolate import interp1d
from typeguard import typechecked
from species.core.box import IsochroneBox, ColorMagBox, ColorColorBox
from species.read.read_filter import ReadFilter
from species.read.read_object import ReadObject
from species.util.core_util import print_section
from species.util.dust_util import calc_reddening, ism_extinction
from species.util.model_util import convert_model_name
from species.util.plot_util import (
field_bounds_ticks,
sptype_to_index,
remove_color_duplicates,
)
[docs]
@typechecked
def plot_color_magnitude(
boxes: list,
objects: Optional[
Union[
List[Tuple[str, str, str, str]],
List[Tuple[str, str, str, str, Optional[dict], Optional[dict]]],
]
] = None,
mass_labels: Optional[Dict[str, List[Tuple[float, str]]]] = None,
teff_labels: Optional[Dict[str, List[Tuple[float, str]]]] = None,
companion_labels: bool = False,
accretion: bool = False,
reddening: Optional[
List[Tuple[Tuple[str, str], Tuple[str, float], str, float, Tuple[float, float]]]
] = None,
ism_red: Optional[
List[Tuple[Tuple[str, str], str, float, Tuple[float, float]]]
] = None,
field_range: Optional[Tuple[str, str]] = None,
label_x: str = "Color",
label_y: str = "Absolute magnitude",
xlim: Optional[Tuple[float, float]] = None,
ylim: Optional[Tuple[float, float]] = None,
offset: Optional[Tuple[float, float]] = None,
legend: Optional[Union[str, dict, Tuple[float, float]]] = "upper left",
figsize: Optional[Tuple[float, float]] = (4.0, 4.8),
output: Optional[str] = None,
) -> mpl.figure.Figure:
"""
Function for creating a color-magnitude diagram.
Parameters
----------
boxes : list(species.core.box.ColorMagBox, species.core.box.IsochroneBox)
Boxes with the color-magnitude and isochrone data from
photometric libraries, spectral libraries, and/or atmospheric
models. The synthetic data have to be created with
:func:`~species.read.read_isochrone.ReadIsochrone.get_color_magnitude`.
These boxes contain synthetic colors and magnitudes for a
given age and a range of masses.
objects : list(tuple(str, str, str, str)),
list(tuple(str, str, str, str, dict, dict)), None
Tuple with individual objects. The objects require a tuple with
their database tag, the two filter names for the color, and the
filter name for the absolute magnitude. Optionally, a
dictionary with keyword arguments can be provided for the
object's marker and label, respectively. For example,
``{'marker': 'o', 'ms': 10}`` for the marker and
``{'ha': 'left', 'va': 'bottom', 'xytext': (5, 5)})`` for the
label. The parameter is not used if set to ``None``.
mass_labels : dict(str, list(tuple(float, str))), None
Plot labels with masses next to the isochrone data.
The argument is a dictionary. The keys are the isochrone tags
and the values are lists of tuples. Each tuple contains the
mass in :math:`M_\\mathrm{J}` and the position of the label
('left' or 'right), for
example ``{'sonora+0.5': [(10., 'left'), (20., 'right')]}``.
No labels will be shown if the argument is set to ``None`` or
if an isochrone tag is not included in the dictionary. The
tags are stored as the ``iso_tag`` attribute of each
:class:`~species.core.box.ColorColorBox`.
teff_labels : dict(str, list(tuple(float, str))), None
Plot labels with temperatures (K) next to the isochrones. The
argument is a dictionary with the names of the models and a
list with tuples that contain the effective temperatures and
'left' or 'right' to indicate the position of the label (so
similar to the use of mass_labels``). For example,
``{'planck': [(1000., 'left'), (1200., 'right')]``. No labels
are shown if the argument is set to ``None``.
companion_labels : bool
Plot labels with the names of the directly imaged companions.
accretion : bool
Plot accreting, directly imaged objects with a different symbol
than the regular, directly imaged objects. The object names
from ``objects`` will be compared with the data from
`data/companion_data/companion_data.json` to check if a
companion is accreting or not.
reddening : list(tuple(tuple(str, str), tuple(str, float),
str, float, tuple(float, float))), None
Include reddening arrows by providing a list with tuples. Each
tuple contains the filter names for the color, the filter name
and value of the magnitude, the mean particle radius (um), and
the start position (color, mag) of the arrow in the plot, so
``((filter_color_1, filter_color_2), (filter_mag, mag_value),
composition, radius, (x_pos, y_pos))``. The composition can be
either ``'Fe'`` or ``'MgSiO3'`` (both with crystalline
structure). A log-normal size distribution is used with the
specified mean radius and the geometric standard deviation is
fixed to 2. Both ``xlim`` and ``ylim`` need to be set for the
correct rotation of the reddening label. The parameter is not
used if set to ``None``.
ism_red : list(tuple(tuple(str, str), str, float,
tuple(float, float))), None
List with reddening arrows for ISM extinction. Each item in the
list is a tuple that itself contain a tuple with the filter
names for the color, the filter name of the magnitude, the
visual extinction, and the start position (color, mag) of the
arrow in the plot, so ``((filter_color_1, filter_color_2),
filter_mag, A_V, (x_pos, y_pos))``. The parameter is not used
if the argument is set to ``None``.
field_range : tuple(str, str), None
Range of spectral types for which the field objects will be
plotted and labeled at the discrete colorbar. The tuple
should contain the lower and upper value of either the classes,
for example ('K', 'T'), or as subclasses by including
early/late, for example ('late K', 'early T'). The range is
set to the default of 'early M' to 'early Y' if the
argument is set to ``None``.
label_x : str
Label for the x-axis.
label_y : str
Label for the y-axis.
xlim : tuple(float, float), None
Limits for the x-axis. Not used if set to None.
ylim : tuple(float, float), None
Limits for the y-axis. Not used if set to None.
offset : tuple(float, float), None
Offset of the x- and y-axis label.
legend : str, tuple(float, float), dict, None
Legend position or keyword arguments. No legend
is shown if set to ``None``.
figsize : tuple(float, float)
Figure size.
output : str, None
Output filename for the plot. The plot is shown in an
interface window if the argument is set to ``None``.
Returns
-------
matplotlib.figure.Figure
The ``Figure`` object that can be used for further
customization of the plot.
"""
print_section("Plot color-magnitude diagram")
print("Boxes:")
for item in boxes:
if isinstance(item, list):
item_type = item[0].__class__.__name__
print(f" - List with {len(item)} x {item_type}")
else:
print(f" - {item.__class__.__name__}")
if objects is None:
print(f"\nObjects: {objects}")
else:
print("\nObjects:")
for item in objects:
print(f" - {item[0]}: {item[1:]}")
print(f"\nSpectral range: {field_range}")
print(f"Companion labels: {companion_labels}")
print(f"Accretion markers: {accretion}")
print(f"\nMass labels: {mass_labels}")
print(f"Teff labels: {teff_labels}")
if reddening is None:
print(f"\nReddening: {reddening}")
else:
print("\nReddening:")
for item in reddening:
print(f" - {item}")
print(f"\nFigure size: {figsize}")
print(f"Limits x axis: {xlim}")
print(f"Limits y axis: {ylim}")
plt.rcParams["font.family"] = "serif"
plt.rcParams["mathtext.fontset"] = "dejavuserif"
# model_color = ("#234398", "#f6a432", "black")
model_color = (
"tab:blue",
"tab:orange",
"tab:green",
"tab:red",
"tab:purple",
"tab:brown",
"tab:pink",
"tab:olive",
"tab:cyan",
)
model_linestyle = ("-", "--", ":", "-.")
if field_range is None:
field_range = ("early M", "early Y")
if (len(field_range[0]) == 1 and len(field_range[1]) != 1) or (
len(field_range[0]) != 1 and len(field_range[1]) == 1
):
raise ValueError(
"The argument of 'field_range' should "
"be a tuple with either the range of "
"spectral type classes, for example "
"('A', 'M'), or a tuple with the range "
"of subclasses, for example ('early K', "
"'late L')."
)
if len(field_range[0]) == 1 and len(field_range[1]) == 1:
check_subclass = False
else:
check_subclass = True
isochrones = []
planck = []
models = []
empirical = []
for item in boxes:
if isinstance(item, IsochroneBox):
isochrones.append(item)
elif isinstance(item, ColorMagBox):
if item.object_type == "model":
models.append(item)
elif item.library == "planck":
planck.append(item)
else:
empirical.append(item)
else:
raise ValueError(
f"Found a {type(item)} while only ColorMagBox and "
"IsochroneBox objects can be provided to 'boxes'."
)
if empirical:
fig = plt.figure(figsize=figsize)
gridsp = mpl.gridspec.GridSpec(3, 1, height_ratios=[0.2, 0.1, 4.5])
gridsp.update(wspace=0.0, hspace=0.0, left=0, right=1, bottom=0, top=1)
ax1 = plt.subplot(gridsp[2, 0])
ax2 = plt.subplot(gridsp[0, 0])
else:
fig = plt.figure(figsize=figsize)
gridsp = mpl.gridspec.GridSpec(1, 1)
gridsp.update(wspace=0.0, hspace=0.0, left=0, right=1, bottom=0, top=1)
ax1 = plt.subplot(gridsp[0, 0])
ax1.tick_params(
axis="both",
which="major",
colors="black",
labelcolor="black",
direction="in",
width=1,
length=5,
labelsize=12,
top=True,
bottom=True,
left=True,
right=True,
)
ax1.tick_params(
axis="both",
which="minor",
colors="black",
labelcolor="black",
direction="in",
width=1,
length=3,
labelsize=12,
top=True,
bottom=True,
left=True,
right=True,
)
ax1.xaxis.set_major_locator(MultipleLocator(1.0))
ax1.yaxis.set_major_locator(MultipleLocator(1.0))
ax1.xaxis.set_minor_locator(MultipleLocator(0.2))
ax1.yaxis.set_minor_locator(MultipleLocator(0.2))
ax1.set_xlabel(label_x, fontsize=14)
ax1.set_ylabel(label_y, fontsize=14)
ax1.invert_yaxis()
if offset is not None:
ax1.get_xaxis().set_label_coords(0.5, offset[0])
ax1.get_yaxis().set_label_coords(offset[1], 0.5)
else:
ax1.get_xaxis().set_label_coords(0.5, -0.08)
ax1.get_yaxis().set_label_coords(-0.12, 0.5)
if xlim is not None:
ax1.set_xlim(xlim[0], xlim[1])
if ylim is not None:
ax1.set_ylim(ylim[0], ylim[1])
if models is not None:
count = 0
model_dict = {}
for j, item in enumerate(models):
if item.library == "sonora-bobcat":
model_key = item.library + item.iso_tag[-4:]
else:
model_key = item.library
if model_key not in model_dict:
model_dict[model_key] = [count, 0]
count += 1
else:
model_dict[model_key] = [
model_dict[model_key][0],
model_dict[model_key][1] + 1,
]
model_count = model_dict[model_key]
if model_count[1] == 0:
label = convert_model_name(item.library)
if item.library == "sonora-bobcat":
metal = float(item.iso_tag[-4:])
label += f", [M/H] = {metal}"
if item.library == "zhu2015":
ax1.plot(
item.color,
item.magnitude,
marker="x",
ms=5,
linestyle=model_linestyle[model_count[1]],
linewidth=0.6,
color="gray",
label=label,
zorder=0,
)
xlim = ax1.get_xlim()
ylim = ax1.get_ylim()
for i, teff_item in enumerate(item.sptype):
teff_label = (
rf"{teff_item:.0e} $M_\mathregular{{Jup}}^{2}$ yr$^{{-1}}$"
)
if item.magnitude[i] > ylim[1]:
ax1.annotate(
teff_label,
(item.color[i], item.magnitude[i]),
color="gray",
fontsize=8,
ha="left",
va="center",
xytext=(item.color[i] + 0.1, item.magnitude[i] + 0.05),
zorder=3,
)
else:
ax1.plot(
item.color,
item.magnitude,
linestyle=model_linestyle[model_count[1]],
lw=1.0,
color=model_color[model_count[0]],
label=label,
zorder=0,
)
if mass_labels is not None:
interp_magnitude = interp1d(item.mass, item.magnitude)
interp_color = interp1d(item.mass, item.color)
if item.iso_tag in mass_labels:
label_select = mass_labels[item.iso_tag]
else:
label_select = []
for i, mass_item in enumerate(label_select):
if isinstance(mass_item, tuple):
mass_val = mass_item[0]
mass_pos = mass_item[1]
else:
mass_val = mass_item
mass_pos = "right"
pos_color = interp_color(mass_val)
pos_mag = interp_magnitude(mass_val)
# if j == 1 and mass_val == 10.:
# mass_ha = "center"
# mass_xytext = (pos_color, pos_mag-0.2)
if mass_pos == "left":
mass_ha = "right"
mass_xytext = (pos_color - 0.05, pos_mag)
else:
mass_ha = "left"
mass_xytext = (pos_color + 0.05, pos_mag)
mass_label = str(int(mass_val)) + r" M$_\mathregular{J}$"
xlim = ax1.get_xlim()
ylim = ax1.get_ylim()
if (
xlim[0] + 0.2 < pos_color < xlim[1] - 0.2
and ylim[1] + 0.2 < pos_mag < ylim[0] - 0.2
):
ax1.scatter(
pos_color,
pos_mag,
c=model_color[model_count[0]],
s=15,
edgecolor="none",
zorder=0,
)
ax1.annotate(
mass_label,
(pos_color, pos_mag),
color=model_color[model_count[0]],
fontsize=9,
xytext=mass_xytext,
zorder=3,
ha=mass_ha,
va="center",
)
else:
ax1.plot(
item.color,
item.magnitude,
linestyle=model_linestyle[model_count[1]],
linewidth=0.6,
color=model_color[model_count[0]],
zorder=0,
)
if planck is not None:
planck_count = 0
for j, item in enumerate(planck):
if planck_count == 0:
label = convert_model_name(item.library)
else:
label = None
ax1.plot(
item.color,
item.magnitude,
linestyle="--",
linewidth=0.8,
color="gray",
label=label,
zorder=0,
)
if teff_labels is not None and planck_count == 0:
interp_magnitude = interp1d(item.sptype, item.magnitude)
interp_color = interp1d(item.sptype, item.color)
if item.library in teff_labels:
label_select = teff_labels[item.library]
else:
label_select = []
for i, teff_item in enumerate(label_select):
if isinstance(teff_item, tuple):
teff_val = teff_item[0]
teff_pos = teff_item[1]
else:
teff_val = teff_item
teff_pos = "right"
if j == 0 or (j > 0 and teff_val < 20.0):
pos_color = interp_color(teff_val)
pos_mag = interp_magnitude(teff_val)
if teff_pos == "left":
teff_ha = "right"
teff_xytext = (pos_color - 0.05, pos_mag)
else:
teff_ha = "left"
teff_xytext = (pos_color + 0.05, pos_mag)
teff_label = f"{int(teff_val)} K"
xlim = ax1.get_xlim()
ylim = ax1.get_ylim()
if (
xlim[0] + 0.2 < pos_color < xlim[1] - 0.2
and ylim[1] + 0.2 < pos_mag < ylim[0] - 0.2
):
ax1.scatter(
pos_color, pos_mag, c="gray", s=15, ec="none", zorder=0
)
if planck_count == 0:
ax1.annotate(
teff_label,
(pos_color, pos_mag),
color="gray",
fontsize=9,
xytext=teff_xytext,
zorder=3,
ha=teff_ha,
va="center",
)
planck_count += 1
if empirical:
cmap = plt.cm.viridis
bounds, ticks, ticklabels = field_bounds_ticks(field_range, check_subclass)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
for item in empirical:
sptype = item.sptype
color = item.color
magnitude = item.magnitude
names = item.names
if isinstance(sptype, list):
sptype = np.array(sptype)
if item.object_type in ["field", None]:
indices = np.where(sptype != "None")[0]
sptype = sptype[indices]
color = color[indices]
magnitude = magnitude[indices]
spt_disc = sptype_to_index(field_range, sptype, check_subclass)
_, unique = np.unique(color, return_index=True)
sptype = sptype[unique]
color = color[unique]
magnitude = magnitude[unique]
spt_disc = spt_disc[unique]
scat = ax1.scatter(
color,
magnitude,
c=spt_disc,
cmap=cmap,
norm=norm,
s=50,
alpha=0.7,
edgecolor="none",
zorder=2,
)
cb = Colorbar(
ax=ax2,
mappable=scat,
orientation="horizontal",
ticklocation="top",
format="%.2f",
)
cb.ax.tick_params(
width=1, length=5, labelsize=10, direction="in", color="black"
)
cb.set_ticks(ticks)
cb.set_ticklabels(ticklabels)
cb.ax.minorticks_off()
elif item.object_type == "young":
if objects is not None:
object_names = []
for obj_item in objects:
object_names.append(obj_item[0])
indices = remove_color_duplicates(object_names, names)
color = color[indices]
magnitude = magnitude[indices]
ax1.plot(
color,
magnitude,
marker="s",
ms=4,
linestyle="none",
alpha=0.7,
color="gray",
markeredgecolor="black",
label="Young/low-gravity",
zorder=2,
)
# for item in names[indices]:
#
# if item == '2MASSWJ2244316+204343':
# item = '2MASS 2244+2043'
#
# kwargs = {'ha': 'left', 'va': 'center', 'fontsize': 8.5,
# 'xytext': (5., 0.), 'color': 'black'}
#
# ax1.annotate(item, (color, magnitude), zorder=3,
# textcoords='offset points', **kwargs)
if isochrones:
for item in isochrones:
ax1.plot(
item.color, item.magnitude, linestyle="-", linewidth=1.0, color="black"
)
if reddening is not None:
for item in reddening:
ext_1, ext_2 = calc_reddening(
item[0],
item[1],
composition=item[2],
structure="crystalline",
radius_g=item[3],
)
delta_x = ext_1 - ext_2
delta_y = item[1][1]
x_pos = item[4][0] + delta_x
y_pos = item[4][1] + delta_y
ax1.annotate(
"",
(x_pos, y_pos),
xytext=(item[4][0], item[4][1]),
fontsize=8,
arrowprops={"arrowstyle": "->"},
color="black",
zorder=3.0,
)
x_pos_text = item[4][0] + delta_x / 2.0
y_pos_text = item[4][1] + delta_y / 2.0
vector_len = np.sqrt(delta_x**2 + delta_y**2)
if item[2] == "MgSiO3":
dust_species = r"MgSiO$_{3}$"
elif item[2] == "Fe":
dust_species = "Fe"
if (item[3]).is_integer():
red_label = f"{dust_species} ({item[3]:.0f} µm)"
else:
red_label = f"{dust_species} ({item[3]:.1f} µm)"
text = ax1.annotate(
red_label,
(x_pos_text, y_pos_text),
xytext=(7.0 * delta_y / vector_len, 7.0 * delta_x / vector_len),
textcoords="offset points",
fontsize=8.0,
color="black",
ha="center",
va="center",
)
ax1.plot([item[4][0], x_pos], [item[4][1], y_pos], "-", color="white")
sp1 = ax1.transData.transform_point((item[4][0], item[4][1]))
sp2 = ax1.transData.transform_point((x_pos, y_pos))
angle = np.degrees(np.arctan2(sp2[1] - sp1[1], sp2[0] - sp1[0]))
text.set_rotation(angle)
if ism_red is not None:
for item in ism_red:
# Color filters
read_filt_0 = ReadFilter(item[0][0])
read_filt_1 = ReadFilter(item[0][1])
# Magnitude filter
read_filt_2 = ReadFilter(item[1])
mean_wavel = np.array(
[
read_filt_0.mean_wavelength(),
read_filt_1.mean_wavelength(),
read_filt_2.mean_wavelength(),
]
)
ext_mag = ism_extinction(item[2], 3.1, mean_wavel)
delta_x = ext_mag[0] - ext_mag[1]
delta_y = ext_mag[2]
x_pos = item[3][0] + delta_x
y_pos = item[3][1] + delta_y
ax1.annotate(
"",
(x_pos, y_pos),
xytext=(item[3][0], item[3][1]),
fontsize=8,
arrowprops={"arrowstyle": "->"},
color="black",
zorder=3.0,
)
x_pos_text = item[3][0] + delta_x / 2.0
y_pos_text = item[3][1] + delta_y / 2.0
vector_len = np.sqrt(delta_x**2 + delta_y**2)
if (item[2]).is_integer():
red_label = rf"A$_\mathregular{{V}}$ = {item[2]:.0f}"
else:
red_label = rf"A$_\mathregular{{V}}$ = {item[2]:.1f}"
text = ax1.annotate(
red_label,
(x_pos_text, y_pos_text),
xytext=(8.0 * delta_y / vector_len, 8.0 * delta_x / vector_len),
textcoords="offset points",
fontsize=8.0,
color="black",
ha="center",
va="center",
)
ax1.plot([item[3][0], x_pos], [item[3][1], y_pos], "-", color="white")
sp1 = ax1.transData.transform_point((item[3][0], item[3][1]))
sp2 = ax1.transData.transform_point((x_pos, y_pos))
angle = np.degrees(np.arctan2(sp2[1] - sp1[1], sp2[0] - sp1[0]))
text.set_rotation(angle)
if objects is not None:
for i, item in enumerate(objects):
objdata = ReadObject(item[0])
objcolor1 = objdata.get_photometry(item[1])
objcolor2 = objdata.get_photometry(item[2])
if objcolor1.ndim == 2:
print(
f"Found {objcolor1.shape[1]} values for filter {item[1]} of {item[0]}"
)
print(
f"so using the first value: {objcolor1[0, 0]} +/- {objcolor1[1, 0]} mag"
)
objcolor1 = objcolor1[:, 0]
if objcolor2.ndim == 2:
print(
f"Found {objcolor2.shape[1]} values for filter {item[2]} of {item[0]}"
)
print(
f"so using the first value: {objcolor2[0, 0]} +/- {objcolor2[1, 0]} mag"
)
objcolor2 = objcolor2[:, 0]
abs_mag, abs_err = objdata.get_absmag(item[3])
if isinstance(abs_mag, np.ndarray):
abs_mag = abs_mag[0]
abs_err = abs_err[0]
colorerr = np.sqrt(objcolor1[1] ** 2 + objcolor2[1] ** 2)
x_color = objcolor1[0] - objcolor2[0]
species_folder = str(pathlib.Path(__file__).parent.resolve())[:-4]
data_file = os.path.join(
species_folder, "data/companion_data/companion_data.json"
)
with open(data_file, "r", encoding="utf-8") as json_file:
comp_data = json.load(json_file)
if len(item) > 4 and item[4] is not None:
kwargs = item[4]
else:
kwargs = {
"marker": "o",
"ms": 5.0,
"color": "black",
"mfc": "white",
"mec": "black",
"label": "Companions",
}
if (
accretion
and item[0] in comp_data
and comp_data[item[0]]["accretion"]
):
kwargs["marker"] = "X"
kwargs["ms"] = 7.0
kwargs["label"] = "Accreting"
ax1.errorbar(
x_color, abs_mag, yerr=abs_err, xerr=colorerr, zorder=3, **kwargs
)
if companion_labels:
if len(item) > 4 and item[5] is not None:
kwargs = item[5]
else:
kwargs = {
"ha": "left",
"va": "bottom",
"fontsize": 8.5,
"xytext": (5.0, 5.0),
"color": "black",
}
ax1.annotate(
objdata.object_name,
(x_color, abs_mag),
zorder=3,
textcoords="offset points",
**kwargs,
)
if legend is not None:
handles, labels = ax1.get_legend_handles_labels()
# Prevent duplicates
by_label = dict(zip(labels, handles))
if handles:
if isinstance(legend, (str, tuple)):
ax1.legend(
by_label.values(),
by_label.keys(),
loc=legend,
fontsize=8.5,
frameon=False,
numpoints=1,
)
else:
ax1.legend(by_label.values(), by_label.keys(), **legend)
if output is None:
plt.show()
else:
print(f"\nOutput: {output}")
plt.savefig(output, bbox_inches="tight")
return fig
[docs]
@typechecked
def plot_color_color(
boxes: list,
objects: Optional[
Union[
List[Tuple[str, Tuple[str, str], Tuple[str, str]]],
List[
Tuple[
str,
Tuple[str, str],
Tuple[str, str],
Optional[dict],
Optional[dict],
]
],
]
] = None,
mass_labels: Optional[Dict[str, List[Tuple[float, str]]]] = None,
teff_labels: Optional[Dict[str, List[Tuple[float, str]]]] = None,
companion_labels: bool = False,
reddening: Optional[
List[
Tuple[
Tuple[str, str],
Tuple[str, str],
Tuple[str, float],
str,
float,
Tuple[float, float],
]
]
] = None,
field_range: Optional[Tuple[str, str]] = None,
label_x: str = "Color",
label_y: str = "Color",
xlim: Optional[Tuple[float, float]] = None,
ylim: Optional[Tuple[float, float]] = None,
offset: Optional[Tuple[float, float]] = None,
legend: Optional[Union[str, dict, Tuple[float, float]]] = "upper left",
figsize: Optional[Tuple[float, float]] = (4.0, 4.3),
output: Optional[str] = None,
) -> mpl.figure.Figure:
"""
Function for creating a color-color diagram.
Parameters
----------
boxes : list(species.core.box.ColorColorBox, species.core.box.IsochroneBox)
Boxes with the color-color from photometric libraries,
spectral libraries, isochrones, and/or atmospheric models.
objects : tuple(tuple(str, tuple(str, str), tuple(str, str))),
tuple(tuple(str, tuple(str, str), tuple(str, str), dict, dict)), None
Tuple with individual objects. The objects require a tuple
with their database tag, the two filter names for the first
color, and the two filter names for the second color.
Optionally, a dictionary with keyword arguments can be provided
for the object's marker and label, respectively. For
example, ``{'marker': 'o', 'ms': 10}`` for the marker
and ``{'ha': 'left', 'va': 'bottom', 'xytext': (5, 5)})``
for the label. The parameter is not used if set to ``None``.
mass_labels : dict(str, list(tuple(float, str))), None
Plot labels with masses next to the isochrone data.
The argument is a dictionary. The keys are the isochrone tags
and the values are lists of tuples. Each tuple contains the
mass in :math:`M_\\mathrm{J}` and the position of the label
('left' or 'right), for
example ``{'sonora+0.5': [(10., 'left'), (20., 'right')]}``.
No labels will be shown if the argument is set to ``None`` or
if an isochrone tag is not included in the dictionary. The
tags are stored as the ``iso_tag`` attribute of each
:class:`~species.core.box.ColorColorBox`.
teff_labels : dict(str, list(tuple(float, str))), None
Plot labels with temperatures (K) next to the isochrones. The
argument is a dictionary with the names of the models and a
list with tuples that contain the effective temperatures and
'left' or 'right' to indicate the position of the label (so
similar to the use of mass_labels``). For example,
``{'planck': [(1000., 'left'), (1200., 'right')]``. No labels
are shown if the argument is set to ``None``.
companion_labels : bool
Plot labels with the names of the directly imaged companions.
reddening : list(tuple(tuple(str, str), tuple(str, str),
tuple(str, float), str, float, tuple(float, float)), None
Include reddening arrows by providing a list with tuples.
Each tuple contains the filter names for the color, the filter
name for the magnitude, the particle radius (um), and the start
position (color, mag) of the arrow in the plot, so
(filter_color_1, filter_color_2, filter_mag, composition,
radius, (x_pos, y_pos)). The composition can be either 'Fe' or
'MgSiO3' (both with crystalline structure). The parameter is
not used if set to ``None``.
field_range : tuple(str, str), None
Range of spectral types for which the field objects will be
plotted and labeled at the discrete colorbar. The tuple
should contain the lower and upper value of either the classes,
for example ('K', 'T'), or as subclasses by including
early/late, for example ('late K', 'early T'). The range is
set to the default of 'early M' to 'early Y' if the
argument is set to ``None``.
label_x : str
Label for the x-axis.
label_y : str
Label for the y-axis.
xlim : tuple(float, float)
Limits for the x-axis.
ylim : tuple(float, float)
Limits for the y-axis.
offset : tuple(float, float), None
Offset of the x- and y-axis label.
legend : str, tuple(float, float), dict, None
Legend position or dictionary with keyword arguments.
No legend is shown if the argument is set to ``None``.
figsize : tuple(float, float)
Figure size.
output : str, None
Output filename for the plot. The plot is shown in an
interface window if the argument is set to ``None``.
Returns
-------
matplotlib.figure.Figure
The ``Figure`` object that can be used for further
customization of the plot.
"""
print_section("Plot color-color diagram")
print("Boxes:")
for item in boxes:
if isinstance(item, list):
item_type = item[0].__class__.__name__
print(f" - List with {len(item)} x {item_type}")
else:
print(f" - {item.__class__.__name__}")
if objects is None:
print(f"\nObjects: {objects}")
else:
print("\nObjects:")
for item in objects:
print(f" - {item[0]}: {item[1:]}")
print(f"\nSpectral range: {field_range}")
print(f"Companion labels: {companion_labels}")
print(f"\nMass labels: {mass_labels}")
print(f"Teff labels: {teff_labels}")
if reddening is None:
print(f"\nReddening: {reddening}")
else:
print("\nReddening:")
for item in reddening:
print(f" - {item}")
print(f"\nFigure size: {figsize}")
print(f"Limits x axis: {xlim}")
print(f"Limits y axis: {ylim}")
plt.rcParams["font.family"] = "serif"
plt.rcParams["mathtext.fontset"] = "dejavuserif"
# model_color = ("#234398", "#f6a432", "black")
model_color = (
"tab:blue",
"tab:orange",
"tab:green",
"tab:red",
"tab:purple",
"tab:brown",
"tab:pink",
"tab:olive",
"tab:cyan",
)
model_linestyle = ("-", "--", ":", "-.")
if field_range is None:
field_range = ("early M", "early Y")
if (len(field_range[0]) == 1 and len(field_range[1]) != 1) or (
len(field_range[0]) != 1 and len(field_range[1]) == 1
):
raise ValueError(
"The argument of 'field_range' should "
"be a tuple with either the range of "
"spectral type classes, for example "
"('A', 'M'), or a tuple with the range "
"of subclasses, for example ('early K', "
"'late L')."
)
if len(field_range[0]) == 1 and len(field_range[1]) == 1:
check_subclass = False
else:
check_subclass = True
isochrones = []
planck = []
models = []
empirical = []
spectra = []
for item in boxes:
if isinstance(item, IsochroneBox):
isochrones.append(item)
elif isinstance(item, ColorColorBox):
if item.object_type == "model":
models.append(item)
elif item.object_type == "spectra":
spectra.append(item)
elif item.library == "planck":
planck.append(item)
else:
empirical.append(item)
else:
raise ValueError(
f"Found a {type(item)} while only ColorColorBox and "
"IsochroneBox objects can be provided to 'boxes'."
)
fig = plt.figure(figsize=figsize)
if empirical:
gridsp = mpl.gridspec.GridSpec(3, 1, height_ratios=[0.2, 0.1, 4.0])
else:
gridsp = mpl.gridspec.GridSpec(1, 1)
gridsp.update(wspace=0.0, hspace=0.0, left=0, right=1, bottom=0, top=1)
if empirical:
ax1 = plt.subplot(gridsp[2, 0])
ax2 = plt.subplot(gridsp[0, 0])
else:
ax1 = plt.subplot(gridsp[0, 0])
ax2 = None
ax1.tick_params(
axis="both",
which="major",
colors="black",
labelcolor="black",
direction="in",
width=1,
length=5,
labelsize=12,
top=True,
bottom=True,
left=True,
right=True,
)
ax1.tick_params(
axis="both",
which="minor",
colors="black",
labelcolor="black",
direction="in",
width=1,
length=3,
labelsize=12,
top=True,
bottom=True,
left=True,
right=True,
)
ax1.xaxis.set_major_locator(MultipleLocator(0.5))
ax1.yaxis.set_major_locator(MultipleLocator(0.5))
ax1.xaxis.set_minor_locator(MultipleLocator(0.1))
ax1.yaxis.set_minor_locator(MultipleLocator(0.1))
ax1.set_xlabel(label_x, fontsize=14)
ax1.set_ylabel(label_y, fontsize=14)
if offset:
ax1.get_xaxis().set_label_coords(0.5, offset[0])
ax1.get_yaxis().set_label_coords(offset[1], 0.5)
else:
ax1.get_xaxis().set_label_coords(0.5, -0.08)
ax1.get_yaxis().set_label_coords(-0.12, 0.5)
if xlim:
ax1.set_xlim(xlim[0], xlim[1])
if ylim:
ax1.set_ylim(ylim[0], ylim[1])
if models is not None:
count = 0
model_dict = {}
for j, item in enumerate(models):
if item.library == "sonora-bobcat":
model_key = item.library + item.iso_tag[-4:]
else:
model_key = item.library
if model_key not in model_dict:
model_dict[model_key] = [count, 0]
count += 1
else:
model_dict[model_key] = [
model_dict[model_key][0],
model_dict[model_key][1] + 1,
]
model_count = model_dict[model_key]
if model_count[1] == 0:
label = convert_model_name(item.library)
if item.library == "sonora-bobcat":
metal = float(item.iso_tag[-4:])
label += f", [M/H] = {metal}"
if item.library == "zhu2015":
ax1.plot(
item.color1,
item.color2,
marker="x",
ms=5,
linestyle=model_linestyle[model_count[1]],
linewidth=0.6,
color="gray",
label=label,
zorder=0,
)
xlim = ax1.get_xlim()
ylim = ax1.get_ylim()
for i, teff_item in enumerate(item.sptype):
teff_label = (
rf"{teff_item:.0e} $M_\mathregular{{Jup}}^{2}$ yr$^{{-1}}$"
)
if item.color2[i] < ylim[1]:
ax1.annotate(
teff_label,
(item.color1[i], item.color2[i]),
color="gray",
fontsize=8,
ha="left",
va="center",
xytext=(item.color1[i] + 0.1, item.color2[i] - 0.05),
zorder=3,
)
else:
ax1.plot(
item.color1,
item.color2,
linestyle=model_linestyle[model_count[1]],
lw=1.0,
color=model_color[model_count[0]],
label=label,
zorder=0,
)
if mass_labels is not None:
interp_color1 = interp1d(item.mass, item.color1)
interp_color2 = interp1d(item.mass, item.color2)
if item.iso_tag in mass_labels:
label_select = mass_labels[item.iso_tag]
else:
label_select = []
for i, mass_item in enumerate(label_select):
mass_val = mass_item[0]
mass_pos = mass_item[1]
pos_color1 = interp_color1(mass_val)
pos_color2 = interp_color2(mass_val)
if mass_pos == "left":
mass_ha = "right"
mass_xytext = (pos_color1 - 0.05, pos_color2)
else:
mass_ha = "left"
mass_xytext = (pos_color1 + 0.05, pos_color2)
mass_label = str(int(mass_val)) + r" M$_\mathregular{J}$"
xlim = ax1.get_xlim()
ylim = ax1.get_ylim()
if (
xlim[0] + 0.2 < pos_color1 < xlim[1] - 0.2
and ylim[0] + 0.2 < pos_color2 < ylim[1] - 0.2
):
ax1.scatter(
pos_color1,
pos_color2,
c=model_color[model_count[0]],
s=15,
edgecolor="none",
zorder=0,
)
ax1.annotate(
mass_label,
(pos_color1, pos_color2),
color=model_color[model_count[0]],
fontsize=9,
xytext=mass_xytext,
ha=mass_ha,
va="center",
zorder=3,
)
else:
warnings.warn(
f"Please use larger axes limits "
f"to include the mass label for "
f"{mass_val} Mjup."
)
else:
ax1.plot(
item.color1,
item.color2,
linestyle=model_linestyle[model_count[1]],
linewidth=0.6,
color=model_color[model_count[0]],
label=None,
zorder=0,
)
if planck is not None:
planck_count = 0
for j, item in enumerate(planck):
if planck_count == 0:
label = convert_model_name(item.library)
ax1.plot(
item.color1,
item.color2,
ls="--",
linewidth=0.8,
color="gray",
label=label,
zorder=0,
)
if teff_labels is not None:
interp_color1 = interp1d(item.sptype, item.color1)
interp_color2 = interp1d(item.sptype, item.color2)
if item.library in teff_labels:
label_select = teff_labels[item.library]
else:
label_select = []
for i, teff_item in enumerate(label_select):
if isinstance(teff_item, tuple):
teff_val = teff_item[0]
teff_pos = teff_item[1]
else:
teff_val = teff_item
teff_pos = "right"
if j == 0 or (j > 0 and teff_val < 20.0):
pos_color1 = interp_color1(teff_val)
pos_color2 = interp_color2(teff_val)
if teff_pos == "left":
teff_ha = "right"
teff_xytext = (pos_color1 - 0.05, pos_color2)
else:
teff_ha = "left"
teff_xytext = (pos_color1 + 0.05, pos_color2)
teff_label = f"{int(teff_val)} K"
xlim = ax1.get_xlim()
ylim = ax1.get_ylim()
if (
xlim[0] + 0.2 < pos_color1 < xlim[1] - 0.2
and ylim[0] + 0.2 < pos_color2 < ylim[1] - 0.2
):
ax1.scatter(
pos_color1,
pos_color2,
c="gray",
s=15,
edgecolor="none",
zorder=0,
)
ax1.annotate(
teff_label,
(pos_color1, pos_color2),
color="gray",
fontsize=9,
xytext=teff_xytext,
zorder=3,
ha=teff_ha,
va="center",
)
else:
ax1.plot(
item.color1, item.color2, ls="--", lw=0.5, color="gray", zorder=0
)
planck_count += 1
if spectra is not None:
spectra_count = 0
for j, item in enumerate(spectra):
if spectra_count == 0:
label = convert_model_name(item.library)
ax1.plot(
item.color1,
item.color2,
ls="--",
linewidth=0.8,
color="tomato",
label=label,
zorder=0,
)
if teff_labels is not None:
interp_color1 = interp1d(item.sptype, item.color1)
interp_color2 = interp1d(item.sptype, item.color2)
if item.library in teff_labels:
label_select = teff_labels[item.library]
else:
label_select = []
for i, teff_item in enumerate(label_select):
if isinstance(teff_item, tuple):
teff_val = teff_item[0]
teff_pos = teff_item[1]
else:
teff_val = teff_item
teff_pos = "right"
if j == 0 or (j > 0 and teff_val < 20.0):
pos_color1 = interp_color1(teff_val)
pos_color2 = interp_color2(teff_val)
if teff_pos == "left":
teff_ha = "right"
teff_xytext = (pos_color1 - 0.05, pos_color2)
else:
teff_ha = "left"
teff_xytext = (pos_color1 + 0.05, pos_color2)
teff_label = f"{int(teff_val)} K"
xlim = ax1.get_xlim()
ylim = ax1.get_ylim()
if (
xlim[0] + 0.2 < pos_color1 < xlim[1] - 0.2
and ylim[0] + 0.2 < pos_color2 < ylim[1] - 0.2
):
ax1.scatter(
pos_color1,
pos_color2,
c="tomato",
s=15,
edgecolor="none",
zorder=0,
)
ax1.annotate(
teff_label,
(pos_color1, pos_color2),
color="tomato",
fontsize=9,
xytext=teff_xytext,
zorder=3,
ha=teff_ha,
va="center",
)
else:
ax1.plot(
item.color1, item.color2, ls="--", lw=0.5, color="tomato", zorder=0
)
spectra_count += 1
if empirical:
cmap = plt.cm.viridis
bounds, ticks, ticklabels = field_bounds_ticks(field_range, check_subclass)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
for item in empirical:
sptype = item.sptype
names = item.names
color1 = item.color1
color2 = item.color2
if isinstance(sptype, list):
sptype = np.array(sptype)
if item.object_type in ["field", None]:
indices = np.where(sptype != "None")[0]
sptype = sptype[indices]
color1 = color1[indices]
color2 = color2[indices]
spt_disc = sptype_to_index(field_range, sptype, check_subclass)
_, unique = np.unique(color1, return_index=True)
sptype = sptype[unique]
color1 = color1[unique]
color2 = color2[unique]
spt_disc = spt_disc[unique]
scat = ax1.scatter(
color1,
color2,
c=spt_disc,
cmap=cmap,
norm=norm,
s=50,
alpha=0.7,
edgecolor="none",
zorder=2,
)
cb = Colorbar(
ax=ax2,
mappable=scat,
orientation="horizontal",
ticklocation="top",
format="%.2f",
)
cb.ax.tick_params(
width=1, length=5, labelsize=10, direction="in", color="black"
)
cb.set_ticks(ticks)
cb.set_ticklabels(ticklabels)
cb.ax.minorticks_off()
elif item.object_type == "young":
if objects is not None:
object_names = []
for obj_item in objects:
object_names.append(obj_item[0])
indices = remove_color_duplicates(object_names, names)
color1 = color1[indices]
color2 = color2[indices]
ax1.plot(
color1,
color2,
marker="s",
ms=4,
linestyle="none",
alpha=0.7,
color="gray",
markeredgecolor="black",
label="Young/low-gravity",
zorder=2,
)
if isochrones:
for item in isochrones:
ax1.plot(
item.colors[0],
item.colors[1],
linestyle="-",
linewidth=1.0,
color="black",
)
if reddening is not None:
for item in reddening:
ext_1, ext_2 = calc_reddening(
item[0],
item[2],
composition=item[3],
structure="crystalline",
radius_g=item[4],
)
ext_3, ext_4 = calc_reddening(
item[1],
item[2],
composition=item[3],
structure="crystalline",
radius_g=item[4],
)
delta_x = ext_1 - ext_2
delta_y = ext_3 - ext_4
x_pos = item[5][0] + delta_x
y_pos = item[5][1] + delta_y
ax1.annotate(
"",
(x_pos, y_pos),
xytext=(item[5][0], item[5][1]),
fontsize=8,
arrowprops={"arrowstyle": "->"},
color="black",
zorder=3.0,
)
x_pos_text = item[5][0] + delta_x / 2.0
y_pos_text = item[5][1] + delta_y / 2.0
vector_len = np.sqrt(delta_x**2 + delta_y**2)
if item[3] == "MgSiO3":
dust_species = r"MgSiO$_{3}$"
elif item[3] == "Fe":
dust_species = "Fe"
if item[4].is_integer():
red_label = f"{dust_species} ({item[4]:.0f} µm)"
else:
red_label = f"{dust_species} ({item[4]:.1f} µm)"
text = ax1.annotate(
red_label,
(x_pos_text, y_pos_text),
xytext=(-7.0 * delta_y / vector_len, 7.0 * delta_x / vector_len),
textcoords="offset points",
fontsize=8.0,
color="black",
ha="center",
va="center",
)
ax1.plot([item[5][0], x_pos], [item[5][1], y_pos], "-", color="white")
sp1 = ax1.transData.transform_point((item[5][0], item[5][1]))
sp2 = ax1.transData.transform_point((x_pos, y_pos))
angle = np.degrees(np.arctan2(sp2[1] - sp1[1], sp2[0] - sp1[0]))
text.set_rotation(angle)
if objects is not None:
for i, item in enumerate(objects):
objdata = ReadObject(item[0])
objphot1 = objdata.get_photometry(item[1][0])
objphot2 = objdata.get_photometry(item[1][1])
objphot3 = objdata.get_photometry(item[2][0])
objphot4 = objdata.get_photometry(item[2][1])
if objphot1.ndim == 2:
print(
f"Found {objphot1.shape[1]} values for "
f"filter {item[1][0]} of {item[0]} "
f"so using the first magnitude: "
f"{objphot1[0, 0]} +/- {objphot1[1, 0]}"
)
objphot1 = objphot1[:, 0]
if objphot2.ndim == 2:
print(
f"Found {objphot2.shape[1]} values for "
f"filter {item[1][1]} of {item[0]} "
f"so using the first magnitude: "
f"{objphot2[0, 0]} +/- {objphot2[1, 0]}"
)
objphot2 = objphot2[:, 0]
if objphot3.ndim == 2:
print(
f"Found {objphot3.shape[1]} values for "
f"filter {item[2][0]} of {item[0]} "
f"so using the first magnitude: "
f"{objphot3[0, 0]} +/- {objphot3[1, 0]}"
)
objphot3 = objphot3[:, 0]
if objphot4.ndim == 2:
print(
f"Found {objphot4.shape[1]} values for "
f"filter {item[2][1]} of {item[0]} "
f"so using the first magnitude: "
f"{objphot4[0, 0]} +/- {objphot4[1, 0]}"
)
objphot4 = objphot4[:, 0]
color1 = objphot1[0] - objphot2[0]
color2 = objphot3[0] - objphot4[0]
error1 = np.sqrt(objphot1[1] ** 2 + objphot2[1] ** 2)
error2 = np.sqrt(objphot3[1] ** 2 + objphot4[1] ** 2)
if len(item) > 3 and item[3] is not None:
kwargs = item[3]
else:
kwargs = {
"marker": "o",
"ms": 5.0,
"color": "black",
"mfc": "white",
"mec": "black",
"label": "Companions",
}
ax1.errorbar(color1, color2, xerr=error1, yerr=error2, zorder=3, **kwargs)
if companion_labels:
if len(item) > 3 and item[4] is not None:
kwargs = item[4]
else:
kwargs = {
"ha": "left",
"va": "bottom",
"fontsize": 8.5,
"xytext": (5.0, 5.0),
"color": "black",
}
ax1.annotate(
objdata.object_name,
(color1, color2),
zorder=3,
textcoords="offset points",
**kwargs,
)
handles, labels = ax1.get_legend_handles_labels()
if legend is not None:
handles, labels = ax1.get_legend_handles_labels()
# Prevent duplicates
by_label = dict(zip(labels, handles))
if handles:
if isinstance(legend, (str, tuple)):
ax1.legend(
by_label.values(),
by_label.keys(),
loc=legend,
fontsize=8.5,
frameon=False,
numpoints=1,
)
else:
ax1.legend(by_label.values(), by_label.keys(), **legend)
if output is None:
plt.show()
else:
print(f"\nOutput: {output}")
plt.savefig(output, bbox_inches="tight")
return fig